One of the significant highlights of the Amazon Web Services (AWS) re:Invent 2017 conference is the company’s IoT Analytics; a fully-managed service that makes the experience of running sophisticated analytics on massive volumes of IoT data flawless. The new AWS system eliminates the worry of cost and complexity typically incurred during the build and deployment of a personal IoT analytics platform. AWS IoT Analytics has rendered an effortless way to run analytics on IoT data, along with gathering ongoing insights to better the experience of decision making for IoT applications and machine learning.

The Complexities of Unstructured Data

Since IoT data is highly unstructured, it became a mission for AWS to simplify data structures so that it would become easier for cognitive computing solutions to analyze the IoT database. This idea is executed through business intelligence tools that are designed to process large unstructured data. IoT data is procured mainly through reasonably noisy processes, which in turn produces extensive and complex data with gaps, corruption, false reading and so on; this data needs to be taken care of before any analysis can occur. Besides, IoT data is often integrated into the context of other data from external sources and must be managed appropriately.

Are you utilizing analytics and the existing information provided by your system to increase problem solving and overcome the obstacles to processing big data? Amazon’s AWS IoT Analytics allows for customers to solve complex problems without complex solutions. Our team here at Idexcel is at the ready and available to work with those who want to ensure they are getting the most out of their AWS setup. Be sure to reach out for our cloud advisory services and accelerate your journey to the cloud.

Analyzing Problems and Providing Solutions

AWS IoT Analytics automates each of these problematic steps that are required to analyze data from IoT devices. IoT Analytics acts as a catalyst that filters, transforms, and enriches information before storing it in a time-series data storage for analysis. The service can then be customized according to the business: which, how much, and when to use appropriate data. AWS IoT Analytics applies mathematical equations to process and then enrich the data with device-specific metadata. Data is then analyzed by running queries using the built-in SQL query engine. IoT Analytics kick starts the process and provides better scope for outputting high accuracy information. IoT Analytics also exhibits the ability to facilitate machine learning through employing pre-built models of common IoT use cases; it can then quickly respond to probable system failure or system incompatibility and suggest replacement of hardware.

AWS IoT Analytics can keenly examine and scale automatically to support up to petabytes of IoT data; it helps analyze data from millions of devices and build fast, responsive IoT applications without managing different hardware or infrastructures. The service complements the driving forces of current IoT infrastructure with differing advancements.

It is worth noting some of the most important benefits of IoT Analytics include:

Quick and Easy Queries on Massive IoT Data – With the help of a built-in IoT Analytics SQL query engine, it becomes effortless to run ad-hoc queries; this service enables the user to use standard SQL queries to extract data directly from the data store to answer potential questions.

Time-Series Analytics – AWS IoT Analytics also supports time-series interpretations to analyze the performance of devices over time in a recurring pattern, and understand their place and manner as they are being employed. Analytics can continuously monitor device data and suggest maintenance actions as needed. The system can also observe sensors to analyze and react to environmental conditions.

Data Storage Optimized for IoT – AWS IoT Analytics stores processed device data and can deliver fast response times on IoT queries. The source data is automatically stored for later processing or to reprocess it for another use case, creating a more intelligent dataset.

Prepare IoT Data for Analysis – AWS IoT Analytics also performs data preparation that makes it easy to prepare and process your data for analysis. Integrated with AWS IoT Core, the service makes it easier to ingest device data directly from connected devices. IoT Analytics filters the data apart from corruption, false readings, and errors, and then the system performs mathematical transformations of message data. Using conditional statements the analytical service filters data, and then collects specific data required for analysis; it also gives the option of using AWS Lambda functions to enrich device data from external sources.

Tools for Machine Learning – AWS IoT Analytics is well suited for machine learning on IoT data as it has the ability hosts Jupyter notebooks. The administrator can directly connect IoT data to the notebook to build, train, and execute models right from the IoT Analytics console. Machine learning algorithms are applied to data all the more readily, which produces a health score for each device in the fleet.

Automated Scaling with Pay-As-You-Go Pricing – AWS IoT Analytics follows a pay-as-you-go service, with which one can analyze an entire fleet of connected devices without managing hardware or infrastructure. As the administrator’s needs change, they can expand or contract computation power. The data store will also automatically scale up or down, which results in the billing of only employed resources.

AWS has unveiled a new container service that will allow its users to run Kubernetes on AWS server without needing to install and operate a separate Kubernetes cluster. The service can be identified as a major advancement for AWS which will allow the users migrate smoothly, who had, though, previously found Amazon ECS slightly rigid when it yielded optimum results only when operated on AWS’ own server.

Amazon Elastic Container Service for Kubernetes is a managed service that transcends this obstacle. With this cross platform achievement, AWS will certainly attract (or at least keep) its customers for it has eradicated one major obstacle of transferring clusters on personal server of AWS—inter-cloud exchange. Kubernetes is known to be an open-source system used for automating the deployment, scaling, and managing containerized applications. While Kubernetes had previously posed significant challenges to producing applications, where one was required to manage scaling and availability of Kubernetes masters and persistence layer, Amazon EKS has eased this tedious task by rendering an automatic selection of appropriate instance types. It runs them across multiple Availability Zones along with replacing unhealthy masters through constant heath monitoring. Even the patch and upgrade routines of master and worker nodes no longer need to be monitored manually, which required a lot of expertise and, above all, a tremendous amount of manpower and time. Amazon EKS automatically upgrades the nodes and prepares them for high availability. It runs three Kubernetes masters across three Availability Zones to achieve this flawless feat.

Amazon EKS, just like ECS, can be integrated with many AWS services to provide direct scalability and security for various applications, including Elastic Load Balancing for load distribution, IAM for authentication, Amazon VPC for isolation, AWS PrivateLink for private network access, and AWS CloudTrail for logging. It runs the latest version of the open-source Kubernetes software, which allows the user to have all the latest and existing plugins and tools from the Kubernetes community. Due to the absolute compatibility offered with Amazon EKS for application running on standard Kubernetes Environment, the user can easily migrate any standard Kubernetes application to Amazon EKS without any code modification.

Having stated the common properties of Amazon EKS, let’s look at the major benefits for opting it:

Secure
Security is of paramount importance in this cloud based IT world. With more advanced features, the Amazon EKS is loaded with highly advanced security features for the Kubernetes Environments of any managed cloud service. The migrated workers are launched on the user’s Amazon EC2 instances, where no compute resources are exposed to other customers.

It allows the users to manage the Kubernetes cluster using standard Kubernetes tools such as kubectl CLI for managing Kubernetes, through AWS Identity and Access Management (IAM) authenticated public endpoints or through PrivateLink.

Fully Compatible with Kubernetes Community Tools
Since Amazon EKS runs the latest version of the open-source Kubernetes software, all the existing and even newer features, plugins, and applications are supported in it. Applications that are already running in an existing Kubernetes environment will be fully compatible, and can be flawlessly moved to Amazon EKS cluster.

Fully Managed and Highly Available
Amazon EKS eradicates the need to install, manage, and scale personal Kubernetes clusters. With this development, EKS is one step ahead of the ECS. The worker and master clusters of Kubernetes are automatically made highly available which are distributed across three different Availability Zones for each cluster, due to which, worker and master servers start functioning more smoothly than ever before. Amazon EKS manages the multi Availability Zone architecture and delivers resiliency against the loss of an Availability Zone. Furthermore, it automatically detects and replaces unhealthy masters and provides automated version upgrades and patching for the masters.

Amazon EKS integrates IAM with Kubernetes which enables the user to register IAM entities with the native authentication system in Kubernetes. The user no longer has to worry about manually setting up credentials for authenticating with the Kubernetes masters which also allows IAM to directly authenticate with the master itself as well as granularly control access to the public endpoint with regards to the targeted Kubernetes masters.

Besides that, it also gives the option of using PrivateLink to access Kubernetes masters directly from personal Amazon VPC. With PrivateLink, Kubernetes masters and Amazon EKS service endpoint appear as an elastic network interface with private IP addresses in Amazon VPC, which opens the threshold for accessing the Kubernetes masters and the Amazon EKS service directly from Amazon VPC, without using public IP addresses or requiring the traffic to traverse the internet.

Amazon Elastic Container Service (ECS) is a newly developed, highly scalable and high-performance container orchestration service that supports Docker and allows users to effortlessly run and scale containerized applications on the Amazon Web Services (AWS) platform. ECS removes the need for users to install and operate container orchestration software, manage and scale clusters of virtual machines, or schedule containers on said virtual machines.

ECS is a service that introduces simplicity while running application containers in an accessible manner across multiple availability zones within a region. Users can create Amazon ECS clusters within new or existing virtual PCs. After building a cluster, users can define task definitions and services that specify running Docker container images have to across selected clusters. Container images are stored in and pulled from container registries, which exist within or outside the existing AWS infrastructure.

For vaster control, users can host tasks on a cluster of Amazon Elastic Compute Cloud (EC2) instances; this enables users to schedule the placement of containers across clusters based on resource needs, isolation policies, and availability requirements. ECS is a useful option when creating consistent deployment and build experiences, along with managing Extract-Transform-Load (ETL) workloads. Users can also develop sophisticated application architectures on a micro-services model if desired.

ECS allows users to launch and stop Docker-enabled applications with simple API calls. Perform a query about the state of an application or access additional features such as Identity and Access Management (IAM) roles, security groups, load balancers, CloudWatch Events, CloudFormation templates, and CloudTrail logs.

Recent IT developments have signaled an increasing dependency over smart cloud containers, and that is where Amazon ECS has become an essential pick. Firms are seeking more efficient and ready-to-go solutions that do not add any additional obstacle to an organizational pace. Amazon ECS offers various advantages and customization options including:

Containers Without Infrastructure Management
Amazon ECS features AWS Fargate, which enables users to deploy and manage containers without having to maintain any of the embedded underlying infrastructures. Utilizing AWS Fargate technology, users no longer need to select Amazon EC2 instance types, provision, or scale clusters of virtual machines to run containers. Fargate gives ample time for users to focus on building and running applications without having to worry about the underlying infrastructure.

Containerize Everything
Amazon ECS lets users quickly build various types of containerized applications, from long-running applications and micro-services to batch jobs and machine learning applications. ECS can migrate legacy Linux or Windows applications from on-premise solutions to the cloud and run them as containerized applications.

Secure Infrastructure
Amazon ECS provides the option of launching containers in one’s own Amazon VPC, allowing them to use the VPC security groups and network ACLs. None of the available resources expose themselves to other customers, which makes data all the more secure; it also enables users to assign granular access permissions for each of the containers using IAM to exhibit restriction on access to each service and accessible resources that a container has. This intricate level of isolation permits users to use Amazon ECS to build highly secure and reliable applications.

Performance at Scale
Amazon ECS is a product of gradually developed engineering over a period of years. Built on technology developed from many years of experience, ECS can run highly scalable services. Users can launch various Docker containers in seconds using Amazon ECS with no further introduction of complexity.

Compliment Other AWS Services
Amazon ECS is a product that works well with other AWS services and renders a complete solution for running a wide range of containerized applications. ECS can seamlessly integrate with services such as Elastic Load Balancing, Amazon VPC, AWS RDS, AWS IAM, Amazon ECR, AWS Batch, Amazon CloudWatch, AWS CloudFormation, AWS CodeStar, and AWS CloudTrail, among others.

It is important to highlight that Amazon ECS, when integrated with other AWS Services, will provide the best solution for running a wide range of containerized applications or services instead. Other popular container services such as Kubernetes and Mesos can also be efficiently run on AWS EC2.